Motivated by recent work on deep neural network (DNN)-based image compression
methods showing potential improvements in image quality, savings in storage, and …

Deep neural networks (DNN) have been widely applied in many computer vision problems.
These tasks are often conducted on input images with high quality without consideration of …

In this paper, we propose a novel variable-rate learned image compression framework with
a conditional autoencoder. Previous learning-based image compression methods mostly …

Deep learning, eg, convolutional neural networks (CNNs), has achieved great success in
image processing and computer vision especially in high-level vision applications, such as …

In recent deep image compression neural networks, the entropy model plays a critical role in
estimating the prior distribution of deep image encodings. Existing methods combine …

We propose a versatile deep image compression network based on Spatial Feature
Transform (SFT), which takes a source image and a corresponding quality map as inputs …

Deep learning-based image compressors are actively being explored in an effort to
supersede conventional image compression algorithms, such as JPEG. Conventional and …

Neural image compression leverages deep neural networks to outperform traditional image
codecs in rate-distortion performance. However, the resulting models are also heavy …

As one of most fascinating machine learning techniques, deep neural network (DNN) has
demonstrated excellent performance in various intelligent tasks such as image classification …

Learning-based lossy image compression usually involves the joint optimization of rate-
distortion performance, and requires to cope with the spatial variation of image content and …